1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
13/1/2025 Comida 67387 Tami Supermercado
20/1/2025 Comida 21692 Andrés gnoccis
20/1/2025 Comida 86884 Tami Supermercado
21/1/2025 Comida 21525 Andrés piwen
23/1/2025 VTR 21990 Andrés NA
25/1/2025 Diosi 20000 Andrés arena diosi
27/1/2025 Comida 71516 Tami Supermercado
30/1/2025 Electricidad 55000 Andrés NA
6/2/2025 Comida 52730 Andrés supermercado (no cobre el otro de 25k pq muchas son cosas mías)
9/2/2025 Comida 12500 Andrés NA
17/2/2025 Comida 7940 Andrés NA
18/2/2025 Electricidad 64888 Andrés la puse por adelantado para que no se me olvide
18/2/2025 Comida 17820 Tami Supermercado
23/2/2025 Comida 86908 Tami Supermercado
27/2/2025 Comida 10000 Andrés NA
26/2/2025 Comida 4620 Andrés NA
1/3/2025 Comida 2300 Tami Supermercado
2/3/2025 Comida 102058 Tami Supermercado
3/3/2025 Comida 9370 Andrés NA
9/3/2025 Comida 61916 Tami Supermercado
11/3/2025 Comida 27021 Andrés NA
11/3/2025 Enceres 13190 Tami 40 rollos confort
15/3/2025 Comida 78061 Tami Supermercado
17/3/2025 Electricidad 52458 Andrés NA
17/3/2025 VTR 22000 Andrés NA
21/3/2025 Agua 19562 Andrés NA
22/3/2025 Comida 76766 Tami Supermercado
21/3/2025 Diosi 18500 Andrés antiparasitario
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 9.9612e+08   2    5.0451 0.0066 ** 
## lag_depvar    2.6285e+11   1 2662.5790 <2e-16 ***
## Residuals     8.1149e+10 822                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -1893.199 16350.88 0.1509127
## 2-0 31344.993 23132.310 39557.68 0.0000000
## 2-1 24116.155 19357.799 28874.51 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
## 30   28331.57             0   28706.00
## 31   25617.86             0   28331.57
## 32   27223.29             0   25617.86
## 33   31622.57             0   27223.29
## 34   32021.43             0   31622.57
## 35   33634.57             0   32021.43
## 36   30784.86             0   33634.57
## 37   34770.57             0   30784.86
## 38   38443.00             1   34770.57
## 39   35073.00             1   38443.00
## 40   31422.29             1   35073.00
## 41   30103.29             1   31422.29
## 42   19319.29             1   30103.29
## 43   27926.29             1   19319.29
## 44   30715.43             1   27926.29
## 45   31962.29             1   30715.43
## 46   39790.14             1   31962.29
## 47   39211.57             1   39790.14
## 48   44548.57             1   39211.57
## 49   49398.00             1   44548.57
## 50   41039.00             1   49398.00
## 51   34821.29             1   41039.00
## 52   29123.57             1   34821.29
## 53   21275.71             1   29123.57
## 54   28476.14             1   21275.71
## 55   24561.86             1   28476.14
## 56   20323.57             1   24561.86
## 57   25370.00             1   20323.57
## 58   26811.86             1   25370.00
## 59   27151.86             1   26811.86
## 60   27623.29             1   27151.86
## 61   22896.57             1   27623.29
## 62   41889.29             1   22896.57
## 63   44000.14             1   41889.29
## 64   38558.00             1   44000.14
## 65   43373.86             1   38558.00
## 66   49001.00             1   43373.86
## 67   61213.29             1   49001.00
## 68   58939.57             1   61213.29
## 69   42046.86             1   58939.57
## 70   39191.71             1   42046.86
## 71   42646.43             1   39191.71
## 72   36121.57             1   42646.43
## 73   30915.57             1   36121.57
## 74   20273.43             1   30915.57
## 75   23938.29             1   20273.43
## 76   19274.29             1   23938.29
## 77   21662.29             1   19274.29
## 78   15819.00             1   21662.29
## 79   18126.14             1   15819.00
## 80   17240.71             1   18126.14
## 81   16127.71             1   17240.71
## 82   13917.14             1   16127.71
## 83   15379.86             1   13917.14
## 84   19510.14             1   15379.86
## 85   24567.29             1   19510.14
## 86   25700.43             1   24567.29
## 87   25729.00             1   25700.43
## 88   26435.00             1   25729.00
## 89   31157.14             1   26435.00
## 90   29818.43             1   31157.14
## 91   30962.43             1   29818.43
## 92   28746.71             1   30962.43
## 93   27830.71             1   28746.71
## 94   28252.14             1   27830.71
## 95   28717.57             1   28252.14
## 96   21365.43             1   28717.57
## 97   24816.86             1   21365.43
## 98   16838.57             1   24816.86
## 99   15529.14             1   16838.57
## 100  13286.29             1   15529.14
## 101  13629.43             1   13286.29
## 102  14404.86             1   13629.43
## 103  19524.86             1   14404.86
## 104  18475.71             1   19524.86
## 105  22495.00             1   18475.71
## 106  22254.57             1   22495.00
## 107  24173.29             1   22254.57
## 108  27466.43             1   24173.29
## 109  24602.43             1   27466.43
## 110  20531.14             1   24602.43
## 111  20846.43             1   20531.14
## 112  23875.71             1   20846.43
## 113  36312.71             1   23875.71
## 114  34244.00             1   36312.71
## 115  36347.43             1   34244.00
## 116  39779.71             1   36347.43
## 117  42018.71             1   39779.71
## 118  39372.57             1   42018.71
## 119  33444.00             1   39372.57
## 120  29255.86             1   33444.00
## 121  31640.14             1   29255.86
## 122  29671.14             1   31640.14
## 123  31023.71             1   29671.14
## 124  39723.43             1   31023.71
## 125  39314.14             1   39723.43
## 126  38239.86             1   39314.14
## 127  34649.43             1   38239.86
## 128  36688.43             1   34649.43
## 129  42867.57             1   36688.43
## 130  42226.86             1   42867.57
## 131  32155.14             1   42226.86
## 132  33603.00             1   32155.14
## 133  37254.43             1   33603.00
## 134  33145.57             1   37254.43
## 135  31299.43             1   33145.57
## 136  30252.00             1   31299.43
## 137  26310.71             1   30252.00
## 138  27929.86             1   26310.71
## 139  27666.14             1   27929.86
## 140  25017.57             1   27666.14
## 141  27335.00             1   25017.57
## 142  25760.71             1   27335.00
## 143  18436.86             1   25760.71
## 144  21906.00             1   18436.86
## 145  19418.14             1   21906.00
## 146  22826.14             1   19418.14
## 147  23444.29             1   22826.14
## 148  25264.86             1   23444.29
## 149  25473.29             1   25264.86
## 150  27366.86             1   25473.29
## 151  28855.86             1   27366.86
## 152  32326.86             1   28855.86
## 153  27141.43             1   32326.86
## 154  26297.71             1   27141.43
## 155  23499.14             1   26297.71
## 156  30246.29             1   23499.14
## 157  39931.86             1   30246.29
## 158  38020.43             2   39931.86
## 159  35004.00             2   38020.43
## 160  40750.86             2   35004.00
## 161  42363.29             2   40750.86
## 162  46273.57             2   42363.29
## 163  41083.29             2   46273.57
## 164  35711.29             2   41083.29
## 165  41921.71             2   35711.29
## 166  60583.29             2   41921.71
## 167  63115.57             2   60583.29
## 168  61300.14             2   63115.57
## 169  57666.43             2   61300.14
## 170  55834.00             2   57666.43
## 171  58927.71             2   55834.00
## 172  57810.57             2   58927.71
## 173  48987.14             2   57810.57
## 174  52219.29             2   48987.14
## 175  56503.57             2   52219.29
## 176  56545.00             2   56503.57
## 177  64705.57             2   56545.00
## 178  53833.29             2   64705.57
## 179  50114.00             2   53833.29
## 180  39592.43             2   50114.00
## 181  29907.29             2   39592.43
## 182  33923.29             2   29907.29
## 183  45489.00             2   33923.29
## 184  44866.29             2   45489.00
## 185  51680.57             2   44866.29
## 186  58257.00             2   51680.57
## 187  70600.57             2   58257.00
## 188  76648.00             2   70600.57
## 189  69430.14             2   76648.00
## 190  69651.57             2   69430.14
## 191  77745.14             2   69651.57
## 192  72795.86             2   77745.14
## 193  67670.71             2   72795.86
## 194  55357.86             2   67670.71
## 195  48524.00             2   55357.86
## 196  50154.43             2   48524.00
## 197  45111.57             2   50154.43
## 198  36147.00             2   45111.57
## 199  43501.57             2   36147.00
## 200  41472.43             2   43501.57
## 201  41058.00             2   41472.43
## 202  41605.57             2   41058.00
## 203  49382.86             2   41605.57
## 204  59558.57             2   49382.86
## 205  59134.57             2   59558.57
## 206  61109.00             2   59134.57
## 207  63004.43             2   61109.00
## 208  67344.29             2   63004.43
## 209  78180.86             2   67344.29
## 210  69117.86             2   78180.86
## 211  55597.57             2   69117.86
## 212  49426.14             2   55597.57
## 213  39119.43             2   49426.14
## 214  35636.86             2   39119.43
## 215  39201.14             2   35636.86
## 216  27777.00             2   39201.14
## 217  47207.00             2   27777.00
## 218  55587.29             2   47207.00
## 219  56619.71             2   55587.29
## 220  82679.86             2   56619.71
## 221  91259.57             2   82679.86
## 222  93552.71             2   91259.57
## 223 102242.71             2   93552.71
## 224  91884.00             2  102242.71
## 225  85013.86             2   91884.00
## 226  84535.29             2   85013.86
## 227  80700.43             2   84535.29
## 228  79740.57             2   80700.43
## 229  85163.14             2   79740.57
## 230  86724.86             2   85163.14
## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 638  63528.57             2   67381.43
## 639  49682.86             2   63528.57
## 640  47815.00             2   49682.86
## 641  46546.14             2   47815.00
## 642  44808.71             2   46546.14
## 643  42959.57             2   44808.71
## 644  46023.86             2   42959.57
## 645  51309.57             2   46023.86
## 646  68447.29             2   51309.57
## 647  84959.29             2   68447.29
## 648  81666.29             2   84959.29
## 649  82700.86             2   81666.29
## 650  89422.14             2   82700.86
## 651 104812.71             2   89422.14
## 652  98812.71             2  104812.71
## 653  64779.86             2   98812.71
## 654  61862.86             2   64779.86
## 655  58376.43             2   61862.86
## 656  59503.57             2   58376.43
## 657  55429.43             2   59503.57
## 658  44454.57             2   55429.43
## 659  47184.00             2   44454.57
## 660  52126.71             2   47184.00
## 661  51202.00             2   52126.71
## 662  64437.14             2   51202.00
## 663  64297.14             2   64437.14
## 664  64628.57             2   64297.14
## 665  51413.14             2   64628.57
## 666  52969.43             2   51413.14
## 667  54135.29             2   52969.43
## 668  48799.43             2   54135.29
## 669  41907.86             2   48799.43
## 670  45382.00             2   41907.86
## 671  42633.29             2   45382.00
## 672  46624.71             2   42633.29
## 673  44051.86             2   46624.71
## 674  35852.86             2   44051.86
## 675  29737.71             2   35852.86
## 676  29734.86             2   29737.71
## 677  32881.71             2   29734.86
## 678  38298.57             2   32881.71
## 679  40886.14             2   38298.57
## 680  38601.86             2   40886.14
## 681  38628.86             2   38601.86
## 682  39142.57             2   38628.86
## 683  32666.14             2   39142.57
## 684  39911.57             2   32666.14
## 685  39336.29             2   39911.57
## 686  39678.86             2   39336.29
## 687  41963.14             2   39678.86
## 688  54220.57             2   41963.14
## 689  63901.86             2   54220.57
## 690  73116.00             2   63901.86
## 691  60863.86             2   73116.00
## 692  56293.86             2   60863.86
## 693  52725.00             2   56293.86
## 694  58625.00             2   52725.00
## 695  47513.00             2   58625.00
## 696  40300.14             2   47513.00
## 697  33312.43             2   40300.14
## 698  29556.71             2   33312.43
## 699  27816.71             2   29556.71
## 700  34120.29             2   27816.71
## 701  32132.57             2   34120.29
## 702  32902.57             2   32132.57
## 703  39694.14             2   32902.57
## 704  72501.29             2   39694.14
## 705  79551.14             2   72501.29
## 706  99637.71             2   79551.14
## 707  95424.29             2   99637.71
## 708  98395.14             2   95424.29
## 709 115594.71             2   98395.14
## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 801  49770.86             2   46646.86
## 802  57339.86             2   49770.86
## 803  59799.14             2   57339.86
## 804  53577.14             2   59799.14
## 805  61775.29             2   53577.14
## 806  70627.86             2   61775.29
## 807  57888.43             2   70627.86
## 808  49960.71             2   57888.43
## 809  42923.71             2   49960.71
## 810  47284.86             2   42923.71
## 811  52284.86             2   47284.86
## 812  50191.00             2   52284.86
## 813  36465.86             2   50191.00
## 814  34525.14             2   36465.86
## 815  43199.14             2   34525.14
## 816  52757.43             2   43199.14
## 817  43200.86             2   52757.43
## 818  36772.29             2   43200.86
## 819  29568.00             2   36772.29
## 820  42362.00             2   29568.00
## 821  42566.29             2   42362.00
## 822  39596.00             2   42566.29
## 823  32925.00             2   39596.00
## 824  43416.57             2   32925.00
## 825  52624.86             2   43416.57
## 826  57733.71             2   52624.86
## 827  54120.57             2   57733.71
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   670 53579.26 22295.877
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86  49770.86  57339.86  59799.14  53577.14  61775.29  70627.86
## [806]  57888.43  49960.71  42923.71  47284.86  52284.86  50191.00  36465.86
## [813]  34525.14  43199.14  52757.43  43200.86  36772.29  29568.00  42362.00
## [820]  42566.29  39596.00  32925.00  43416.57  52624.86  57733.71  54120.57
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   2020.48116   4041.05543   -538.71536   2437.51372  -2970.90422    518.28354 
##            8            9           10           11           12           13 
##  -5656.52837  -1186.92980  -3965.16500   -416.09253  -4938.09570  -1606.66600 
##           14           15           16           17           18           19 
##   -897.00971    380.02161  -3240.86652   -375.30173  -2127.82569   6606.64396 
##           20           21           22           23           24           25 
##  -1529.24912  -1208.05759   1476.01420  -1186.86670    234.61085   1694.75554 
##           26           27           28           29           30           31 
##  -7102.89982    948.89283   8193.26525    416.54373    -15.57946  -2401.98294 
##           32           33           34           35           36           37 
##   1575.66846   4571.55134   1124.72508   2389.20263  -1870.65801   4606.16498 
##           38           39           40           41           42           43 
##   4302.71790  -2277.57543  -2982.36760  -1110.05605 -10741.03787   7292.91290 
##           44           45           46           47           48           49 
##   2558.15319   1366.85080   8104.75423    683.37643   6526.14093   6710.17297 
##           50           51           52           53           54           55 
##  -5888.00770  -4798.61132  -5061.04311  -7928.18141   6132.53675  -4076.08183 
##           56           57           58           59           60           61 
##  -4892.65216   3858.71992    889.18669    -31.22839    142.98549  -4995.83321 
##           62           63           64           65           66           67 
##  18128.78972   3636.95933  -3650.41224   5922.75321   7340.06223  14633.31987 
##           68           69           70           71           72           73 
##   1684.10335 -13221.01889  -1309.21202   4641.35641  -4903.47676  -4405.70200 
##           74           75           76           77           78           79 
## -10496.96339   2470.83855  -5396.83614   1068.25014  -6862.53172    552.58283 
##           80           81           82           83           84           85 
##  -2349.65974  -2688.65273  -3926.28312   -531.17380   2320.46433   3767.07305 
##           86           87           88           89           90           91 
##    479.45954   -482.51817    198.50580   4303.49109  -1163.13568   1151.11596 
##           92           93           94           95           96           97 
##  -2064.63836  -1043.74764    178.41228    275.44449  -7483.55781   2394.82632 
##           98           99          100          101          102          103 
##  -8600.56318  -2935.68661  -4033.89255  -1730.13175  -1254.66523   3187.48546 
##          104          105          106          107          108          109 
##  -2337.36103   2599.04431  -1154.88653    974.00100   2589.87892  -3152.85769 
##          110          111          112          113          114          115 
##  -4720.54669   -846.30233   1907.37295  11696.28993  -1244.36303   2667.45461 
##          116          117          118          119          120          121 
##   4261.00539   3499.63554  -1103.75349  -4719.17040  -3724.78813   2320.60817 
##          122          123          124          125          126          127 
##  -1732.64109   1341.15308   8858.50235    844.26688    127.76272  -2523.56732 
##          128          129          130          131          132          133 
##   2654.04482   7050.77367   1008.49453  -8503.13241   1749.02321   4134.79172 
##          134          135          136          137          138          139 
##  -3166.00138  -1420.34212   -853.94485  -3879.60950   1184.85108   -494.25452 
##          140          141          142          143          144          145 
##  -2912.29724   1720.40882  -1879.68226  -7827.36044   2044.01171  -3476.43436 
##          146          147          148          149          150          151 
##   2106.35300   -254.64437   1025.57076   -357.47296   1353.89837   1187.61233 
##          152          153          154          155          156          157 
##   3356.98680  -4862.65413  -1173.46969  -3234.49911   5959.04536   9746.52856 
##          158          159          160          161          162          163 
##  -3647.31761  -4992.85013   3390.85082    -20.39822   2480.36554  -6128.13891 
##          164          165          166          167          168          169 
##  -6962.99427   3943.42647  17176.08429   3395.15459   -633.89905  -2680.63673 
##          170          171          172          173          174          175 
##  -1336.61448   3358.93715   -462.60966  -8309.47567   2635.76314   4094.63606 
##          176          177          178          179          180          181 
##    390.90973   8515.26592  -9490.67197  -3705.83100 -10976.14842 -11463.74530 
##          182          183          184          185          186          187 
##   1018.62756   9073.71181  -1659.29727   5699.34090   6318.98758  12913.70252 
##          188          189          190          191          192          193 
##   8170.86402  -4333.41851   2197.57830  10097.58554  -1926.78358  -2725.45455 
##          194          195          196          197          198          199 
## -10558.11239  -6628.55168    975.76736  -5492.34663 -10048.64970   5142.39924 
##          200          201          202          203          204          205 
##  -3315.82214  -1956.45344  -1046.60477   6252.01543   9629.13091    309.91971 
##          206          207          208          209          210          211 
##   2654.99249   2824.45288   5507.40054  12550.23870  -5985.66795 -11583.43375 
##          212          213          214          215          216          217 
##  -5935.95767 -10847.85062  -5320.69578   1287.91759 -13251.98435  16164.55425 
##          218          219          220          221          222          223 
##   7559.89436   1266.60513  26424.23933  12223.19660   7016.28900  13701.71326 
##          224          225          226          227          228          229 
##  -4253.45894  -2068.41916   3458.61943     42.11070   2434.53559   8696.17657 
##          230          231          232          233          234          235 
##   5517.69113  -2217.35552  -2128.93284   9133.20096 -11808.81470  -7563.64053 
##          236          237          238          239          240          241 
##  -8809.07580 -10356.73640   2835.90229   1105.46465  -8545.86033  -9227.29431 
##          242          243          244          245          246          247 
##   8867.95586  -8007.13133   2255.12808 -10538.93066  -4279.04037   1200.25222 
##          248          249          250          251          252          253 
##    775.08483 -12548.41689   3425.70268   1835.22807   3977.82606   1891.28032 
##          254          255          256          257          258          259 
##  -1409.71567  10889.76119  20611.33461   2890.67773  -4581.67850   3808.43159 
##          260          261          262          263          264          265 
##  -2000.53785   3436.63550  -5156.18966 -11187.25731  -5002.25628   -787.23302 
##          266          267          268          269          270          271 
##  -5453.49615   8520.67477  -4555.65389   3920.62326  -2384.89349   4155.79734 
##          272          273          274          275          276          277 
##    424.56149   7016.63156  -1712.98110  11727.22855  -4906.65840   1413.05947 
##          278          279          280          281          282          283 
##   -686.97092   7539.06823  -5384.52244  -3044.22336 -11565.48833  -2946.05986 
##          284          285          286          287          288          289 
##  18384.41794   7471.02442   2405.92579   -959.94875    579.48136   6072.72970 
##          290          291          292          293          294          295 
##   6545.21580 -19121.36740 -11435.55028  -8386.64733   9421.49266   2804.15716 
##          296          297          298          299          300          301 
##  -1454.13952  27130.65221   9723.87684   4538.74859   9150.63993   2472.92771 
##          302          303          304          305          306          307 
##  -1413.42771   7528.24488 -24674.92495  -3836.78667   -461.94959  -7250.07862 
##          308          309          310          311          312          313 
##  -4230.47225   2686.82933  -9444.67364  -3454.68501  -8401.48397   1372.95264 
##          314          315          316          317          318          319 
##  -3351.30484   1853.90877  -4287.44649  27247.92095  -1027.21980   2991.65288 
##          320          321          322          323          324          325 
##  10522.67676   5251.96679  32032.72431   4676.55879 -21367.99960   1442.14034 
##          326          327          328          329          330          331 
##    764.68200  -6804.66379  -2045.18058 -33566.58779    726.83071  -2460.10920 
##          332          333          334          335          336          337 
##   -244.22271  -3320.31564   3941.01546   -598.65909  -7115.28659  -3258.88744 
##          338          339          340          341          342          343 
##  -2327.76706  -7813.09182   3738.44733  -1505.23652  -1873.24211  -1129.29742 
##          344          345          346          347          348          349 
##     38.72245    337.27728  -1770.25823  -9598.31822 -13335.52412   2221.45109 
##          350          351          352          353          354          355 
##  -4430.77199  -3760.27681  -6078.61704   1662.73618   1278.31999   2631.14064 
##          356          357          358          359          360          361 
##  -3908.71447   -653.73983    533.09580   6858.86429     88.75388   -231.36008 
##          362          363          364          365          366          367 
##   2386.54793  -2959.19309  -1076.71767  -8940.30481  -4789.36985  -6360.61620 
##          368          369          370          371          372          373 
##  -5077.77076  -7367.76171   4920.05854    245.05932   6983.50618  -7808.54394 
##          374          375          376          377          378          379 
##  -2408.81696  -3530.02658  -2601.16380 -12587.89833   1814.54339 -10740.93249 
##          380          381          382          383          384          385 
##   5621.41076   9225.16345   2966.41089  -2578.11055   1429.73084   6557.21550 
##          386          387          388          389          390          391 
##  11191.26205  -6072.30471  -5611.39742   -386.28273   8333.06380   1547.42055 
##          392          393          394          395          396          397 
##  10947.24474 -10198.98161   2496.76746    425.20120    274.54649   -941.09218 
##          398          399          400          401          402          403 
##   -844.63496 -14763.61033   8315.74124  -1420.29697  -1603.43575   6759.07119 
##          404          405          406          407          408          409 
##  -8183.92248  -1509.65773  -2735.89087  -6009.94473  -3022.66868  -4068.97049 
##          410          411          412          413          414          415 
##  -8892.31252   6030.33564   1501.89840  -7525.54049  -7816.89052  14123.10543 
##          416          417          418          419          420          421 
##   3642.19504   4292.78129  -8260.46202  -4933.19632  -2769.56753   2661.12487 
##          422          423          424          425          426          427 
## -14185.50131  -2906.25121  -9207.68991   2936.81279   6876.21825   6432.59297 
##          428          429          430          431          432          433 
##  -4167.39340  -4286.21488  -4872.78078  -1923.73454  -5843.98481  -6739.95760 
##          434          435          436          437          438          439 
##  -6041.97075  -1469.71710   -929.81020  -5064.81935   2502.72811   4737.02769 
##          440          441          442          443          444          445 
##  -5190.86823  -2277.43444   1458.49741  -3970.11452   2712.49763  -6721.20564 
##          446          447          448          449          450          451 
## -12230.78947  -4587.38630   9577.28265  -2152.50394   4635.61871  -6015.41685 
##          452          453          454          455          456          457 
##  -1248.07322    257.15553   2892.03368 -12420.75163   3263.82481  -6828.44536 
##          458          459          460          461          462          463 
##   6416.65805   2871.76844   2349.14991  -4017.33268   1935.23988   -176.54719 
##          464          465          466          467          468          469 
##   1621.77141   -701.69317   3171.07420  -2833.41934   5622.83553  -7148.06211 
##          470          471          472          473          474          475 
##  -3138.03687  -2365.77834  -4815.31511   2862.81711   7647.92539  -6200.90503 
##          476          477          478          479          480          481 
##   1326.09843  -6344.10467  -2984.82499   1880.51067 -13073.08932  -9850.22273 
##          482          483          484          485          486          487 
##  -1267.03276    -50.59415  -1043.53934  -1428.89372  -9675.46588  11032.99664 
##          488          489          490          491          492          493 
##   6118.10511   7272.28887  -5617.31041   5207.96637   9110.35919   5834.35311 
##          494          495          496          497          498          499 
## -13713.57479 -10746.20608  -3578.13235  -1231.65464   -649.37987  -7752.69128 
##          500          501          502          503          504          505 
##    510.92317   4179.37053   5378.24908    504.88562    -78.73621  -7399.38000 
##          506          507          508          509          510          511 
##    437.31984  -5186.07150   1711.88853  -1428.30376  -8287.89277   -704.19748 
##          512          513          514          515          516          517 
##  -2780.39664   -689.22474   1226.62308  -9611.91224  -7850.93313  24221.87135 
##          518          519          520          521          522          523 
##   9656.98134   5672.17792  -5563.00936   2596.18392  16808.43496  11199.71794 
##          524          525          526          527          528          529 
## -24458.79429  -5263.44722  -3914.03594   4407.03853   -540.27274 -11283.96126 
##          530          531          532          533          534          535 
##   4254.18021  13752.29694  -5189.84457   4182.83845   5347.87018  -2016.94191 
##          536          537          538          539          540          541 
##  -4756.11255  -7267.54687  -2263.79148   8167.57804    -61.01162  -8328.39423 
##          542          543          544          545          546          547 
##   1662.53016   -761.13733    206.60688 -11192.58375 -11181.10229   1959.48348 
##          548          549          550          551          552          553 
##   6906.24363  -1448.63794    707.39917  -7857.14069   8455.21214    758.31665 
##          554          555          556          557          558          559 
## -12098.39497   9052.07238   8505.38052    -90.06630   4664.02007  -3781.84532 
##          560          561          562          563          564          565 
##  13917.10645  21252.86813  -6791.29685  -9967.33350   6536.30542    -33.21725 
##          566          567          568          569          570          571 
##   3202.15957  -7635.75409 -17528.58425   6517.07107   6263.42869   1708.10125 
##          572          573          574          575          576          577 
##   2900.48744   1564.19551  -2372.87936  14522.30667  -9898.05401  -6449.60137 
##          578          579          580          581          582          583 
##   8533.86884   2648.63528  -6762.13425   7322.44607  -4012.81993  -2969.10527 
##          584          585          586          587          588          589 
##  15523.00213 -14737.56941   8251.61167   -136.83336  -6415.15165   -928.36073 
##          590          591          592          593          594          595 
##     82.97180 -10821.40589   1671.01494  -7279.22696   2958.53973   8741.60043 
##          596          597          598          599          600          601 
##  -7661.99934   5718.61244   2577.49064   6687.73905  -3383.31520   5971.81571 
##          602          603          604          605          606          607 
##  -8497.16731   2092.25133   1099.64655   2965.05302   1312.41311    212.25026 
##          608          609          610          611          612          613 
##  -5993.61946   7916.89449  -1370.41135  -2752.14983  -3618.02756  -8377.53099 
##          614          615          616          617          618          619 
##  11840.88775   4772.71242  -9494.14260  11484.48922   5865.94569  -5774.48068 
##          620          621          622          623          624          625 
##  26185.26560 -13088.47188  -6981.88796   2986.97574  -4336.63596 -10750.29599 
##          626          627          628          629          630          631 
##  11178.33089 -21794.65144  -2497.45404   8595.69741  11020.76370  -1707.59524 
##          632          633          634          635          636          637 
##  33136.57998  -6846.60873   5493.24990   5165.27464  -2510.15258  -5567.41487 
##          638          639          640          641          642          643 
##  -2134.51585 -12612.21320  -2376.68878  -2012.73830  -2640.98162  -2971.33241 
##          644          645          646          647          648          649 
##   1709.40162   4316.43726  16833.58697  18364.46783    637.32364   4550.50679 
##          650          651          652          653          654          655 
##  10367.41065  19882.49715    428.66192 -28359.23003  -1526.03821  -2462.53954 
##          656          657          658          659          660          661 
##   1712.30274  -3347.14430 -10760.54520   1562.67399   4119.42863  -1126.01305 
##          662          663          664          665          666          667 
##  12917.47885   1207.83492   1661.64602 -11843.50444   1265.19174   1070.60482 
##          668          669          670          671          672          673 
##  -5284.39901  -7511.57295   1986.91195  -3798.76206   2595.48497  -3466.52283 
##          674          675          676          677          678          679 
##  -9416.43180  -8364.32973  -3021.56831    127.78644   2793.78422    646.15113 
##          680          681          682          683          684          685 
##  -3900.08827  -1876.25509  -1386.14315  -8311.64064   4595.22833  -2313.72749 
##          686          687          688          689          690          691 
##  -1468.26381    516.55937  10777.15477   9743.47612  10494.61789  -9812.16805 
##          692          693          694          695          696          697 
##  -3671.82426  -3245.76625   5773.98899 -10495.56011  -7994.74172  -8677.25857 
##          698          699          700          701          702          703 
##  -6324.58650  -4781.48805   3043.12329  -4454.92645  -1947.34441   4171.12315 
##          704          705          706          707          708          709 
##  31041.34006   9412.47683  23336.33905   1564.01583   8218.08733  22820.65171 
##          710          711          712          713          714          715 
##   6458.31662 -18295.83274   4754.99309  -5507.69115   -156.83272    426.09851 
##          716          717          718          719          720          721 
## -17318.66660  -5304.45237   3297.68099  -3053.00034 -13016.14715   4249.61151 
##          722          723          724          725          726          727 
##  -5590.32579    710.84254  -3968.03660 -12479.25619   1341.99614  -1895.61964 
##          728          729          730          731          732          733 
##  -9806.69763  17244.08176   1737.41902  -2762.24343   5677.11804  -8672.59394 
##          734          735          736          737          738          739 
##   -759.97711   8101.57234 -15393.86823  -5943.77511   7377.81415  -4821.03784 
##          740          741          742          743          744          745 
##    126.48895   1792.21798  -1993.39042  -5204.99226   6378.19312  -6314.37951 
##          746          747          748          749          750          751 
##  22662.85588   7777.24105  -1999.97004  -7338.26076  23372.06425  -4347.15154 
##          752          753          754          755          756          757 
##   1345.80318 -14471.30638  56069.33486  26858.77510  15030.40407 -10708.15236 
##          758          759          760          761          762          763 
##  10557.04911   7265.82798   5762.99798 -46433.16285 -16193.06368    950.63097 
##          764          765          766          767          768          769 
##  -2534.21373  -3474.05275 122828.48584  19268.36107  43679.17637  22541.68935 
##          770          771          772          773          774          775 
##  12033.70435  15807.47063  25689.01138 -98847.94958  -6774.56719 -35847.41005 
##          776          777          778          779          780          781 
##   1728.11354  -1238.82116   3385.80382  -7426.02567  -1455.12001  -1955.45550 
##          782          783          784          785          786          787 
##   3414.30405  -7166.07214  -2246.41280   3910.01090   2255.20867  -2769.38600 
##          788          789          790          791          792          793 
##  -4056.95545   1730.23064   2844.57549    -60.49345  -6712.17504  -5789.96949 
##          794          795          796          797          798          799 
##  -1153.95558  -1269.84015  -7824.05338  -2359.76248  -3274.13039  -2671.47188 
##          800          801          802          803          804          805 
##  10718.35168   2233.12075   7071.24221   2914.00434  -5457.80698   8179.36477 
##          806          807          808          809          810          811 
##   9865.44055 -10612.55951  -7403.96390  -7510.86633   3001.74653   4189.40612 
##          812          813          814          815          816          817 
##  -2275.25534 -14170.03023  -4112.76177   6257.73471   8233.54908  -9678.50165 
##          818          819          820          821          822          823 
##  -7753.09234  -9337.77249   9753.93214  -1225.79625  -4374.66054  -8449.15301 
##          824          825          826          827 
##   7873.94554   7910.91011   4970.24425  -3108.86159 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17248.80  20097.94  24354.86  24072.63  26427.62  23758.43  24475.24  19704.07 
##        10        11        12        13        14        15        16        17 
##  19440.45  16781.38  17559.38  14286.52  14337.72  15002.84  16700.58  15019.44 
##        18        19        20        21        22        23        24        25 
##  16054.83  15427.93  22515.25  21598.63  21078.13  22969.44  22294.96  22947.96 
##        26        27        28        29        30        31        32        33 
##  24795.19  18719.39  20446.73  28289.46  28347.15  28019.84  25647.62  27051.02 
##        34        35        36        37        38        39        40        41 
##  30896.70  31245.37  32655.52  30164.41  34140.28  37350.58  34404.65  31213.34 
##        42        43        44        45        46        47        48        49 
##  30060.32  20633.37  28157.28  30595.43  31685.39  38528.19  38022.43  42687.83 
##        50        51        52        53        54        55        56        57 
##  46927.01  39619.90  34184.61  29203.90  22343.61  28637.94  25216.22  21511.28 
##        58        59        60        61        62        63        64        65 
##  25922.67  27183.09  27480.30  27892.40  23760.50  40363.18  42208.41  37451.10 
##        66        67        68        69        70        71        72        73 
##  41660.94  46579.97  57255.47  55267.88  40500.93  38005.07  41025.05  35321.27 
##        74        75        76        77        78        79        80        81 
##  30770.39  21467.45  24671.12  20594.04  22681.53  17573.56  19590.37  18816.37 
##        82        83        84        85        86        87        88        89 
##  17843.43  15911.03  17189.68  20800.21  25220.97  26211.52  26236.49  26853.65 
##        90        91        92        93        94        95        96        97 
##  30981.56  29811.31  30811.35  28874.46  28073.73  28442.13  28848.99  22422.03 
##        98        99       100       101       102       103       104       105 
##  25439.13  18464.83  17320.18  15359.56  15659.52  16337.37  20813.08  19895.96 
##       106       107       108       109       110       111       112       113 
##  23409.46  23199.28  24876.55  27755.29  25251.69  21692.73  21968.34  24616.42 
##       114       115       116       117       118       119       120       121 
##  35488.36  33679.97  35518.71  38519.08  40476.32  38163.17  32980.65  29319.53 
##       122       123       124       125       126       127       128       129 
##  31403.78  29682.56  30864.93  38469.88  38112.09  37173.00  34034.38  35816.80 
##       130       131       132       133       134       135       136       137 
##  41218.36  40658.28  31853.98  33119.64  36311.57  32719.77  31105.94  30190.32 
##       138       139       140       141       142       143       144       145 
##  26745.01  28160.40  27929.87  25614.59  27640.40  26264.22  19861.99  22894.58 
##       146       147       148       149       150       151       152       153 
##  20719.79  23698.93  24239.29  25830.76  26012.96  27668.24  28969.87  32004.08 
##       154       155       156       157       158       159       160       161 
##  27471.18  26733.64  24287.24  30185.33  41667.75  39996.85  37360.01  42383.68 
##       162       163       164       165       166       167       168       169 
##  43793.21  47211.42  42674.28  37978.29  43407.20  59720.42  61934.04  60347.07 
##       170       171       172       173       174       175       176       177 
##  57170.61  55568.78  58273.18  57296.62  49583.52  52408.94  56154.09  56190.31 
##       178       179       180       181       182       183       184       185 
##  63323.96  53819.83  50568.58  41371.03  32904.66  36415.29  46525.58  45981.23 
##       186       187       188       189       190       191       192       193 
##  51938.01  57686.87  68477.14  73763.56  67453.99  67647.56  74722.64  70396.17 
##       194       195       196       197       198       199       200       201 
##  65915.97  55152.55  49178.66  50603.92  46195.65  38359.17  44788.25  43014.45 
##       202       203       204       205       206       207       208       209 
##  42652.18  43130.84  49929.44  58824.65  58454.01  60179.98  61836.89  65630.62 
##       210       211       212       213       214       215       216       217 
##  75103.53  67181.01  55362.10  49967.28  40957.55  37913.23  41028.98  31042.45 
##       218       219       220       221       222       223       224       225 
##  48027.39  55353.11  56255.62  79036.37  86536.43  88541.00  96137.46  87082.28 
##       226       227       228       229       230       231       232       233 
##  81076.67  80658.32  77306.04  76466.97  81207.17  82572.36  77004.08  72213.80 
##       234       235       236       237       238       239       240       241 
##  77871.24  64510.07  56541.22  48486.45  40092.38  44287.11  46441.29  39887.58 
##       242       243       244       245       246       247       248       249 
##  33562.90  43852.27  38095.30  42033.64  34292.33  32997.32  36655.06  39480.85 
##       250       251       252       253       254       255       256       257 
##  30304.15  36246.20  40050.17  45248.43  47968.57  47460.81  57768.67  75277.61 
##       258       259       260       261       262       263       264       265 
##  75092.54  68398.71  69881.54  66099.79  67546.90  61300.40  50567.83  46592.52 
##       266       267       268       269       270       271       272       273 
##  46802.07  42906.18  51716.23  47986.81  52136.32  50251.63  54321.72  54617.94 
##       274       275       276       277       278       279       280       281 
##  60639.41  58272.06  67951.52  61872.23  62082.40  60430.36  66177.09  59903.37 
##       282       283       284       285       286       287       288       289 
##  56464.92  46010.20  44405.87  61649.69  67183.50  67593.23  65009.09  64095.84 
##       290       291       292       293       294       295       296       297 
##  68099.50  72012.37  52996.12  43091.50  37098.51  47426.84  50670.85  49784.20 
##       298       299       300       301       302       303       304       305 
##  73996.84  79946.25  80614.36  85229.93  83427.28  78454.18  81923.35  56805.22 
##       306       307       308       309       310       311       312       313 
##  53063.81  52743.36  46529.33  43736.88  47342.67  39889.83  38611.06  33168.90 
##       314       315       316       317       318       319       320       321 
##  36956.02  36136.81  39970.88  37953.94  63757.79  61597.49  63222.18  71225.75 
##       322       323       324       325       326       327       328       329 
##  73614.70  99113.73  97490.29  73304.00  72101.03  70457.24  62403.47  59523.73 
##       330       331       332       333       334       335       336       337 
##  29451.60  33141.68  33581.51  35903.03  35243.41  41014.37  42090.72  37335.03 
##       338       339       340       341       342       343       344       345 
##  36548.91  36675.66  31991.41  37994.52  38658.38  38917.01  39793.42  41580.58 
##       346       347       348       349       350       351       352       353 
##  43403.83  43155.32  36095.10  26656.41  32004.77  30864.99  30454.76  28069.55 
##       354       355       356       357       358       359       360       361 
##  32751.68  36508.57  40975.29  39163.03  40424.19  42564.14  49964.53  50515.50 
##       362       363       364       365       366       367       368       369 
##  50717.31  53182.19  50663.86  50108.02  42748.08  39942.90  36117.20  33894.33 
##       370       371       372       373       374       375       376       377 
##  29949.37  37242.37  39530.92  47421.97  41389.39  40836.17  39372.45  38904.90 
##       378       379       380       381       382       383       384       385 
##  29766.17  34367.50  27414.30  35639.41  45979.73  49547.68  47819.84  49812.93 
##       386       387       388       389       390       391       392       393 
##  56037.45  65529.59  58736.11  53200.43  52928.94  60313.72  60837.47  69512.27 
##       394       395       396       397       398       399       400       401 
##  58610.23  60178.23  59738.02  59221.52  57707.35  56468.04  43217.26  51809.01 
##       402       403       404       405       406       407       408       409 
##  50808.72  49774.21  56180.07  48717.23  48027.89  46353.37  42027.53  40857.40 
##       410       411       412       413       414       415       416       417 
##  38919.88  33009.81  40888.24  43816.68  38485.18  33569.89  48452.23  52299.79 
##       418       419       420       421       422       423       424       425 
##  56231.89  48695.62  45016.28  43691.30  47280.36  35691.11  35420.12  29674.76 
##       426       427       428       429       430       431       432       433 
##  35268.64  43602.26  50499.39  47262.50  44329.07  41252.02  41140.13  37615.39 
##       434       435       436       437       438       439       440       441 
##  33750.97  30983.00  32560.24  34410.96  32414.13  37283.83  43493.87  40243.86 
##       442       443       444       445       446       447       448       449 
##  39949.65  42958.26  40842.79  44835.21  40078.65  31104.39  29941.00  41306.22 
##       450       451       452       453       454       455       456       457 
##  40987.52  46642.85  42275.79  42625.70  44247.39  47968.32  37835.18  42688.02 
##       458       459       460       461       462       463       464       465 
##  38107.91  45682.52  49205.14  51827.62  48554.76  50897.26  51098.94  52847.26 
##       466       467       468       469       470       471       472       473 
##  52344.50  55290.42  52616.74  57671.63  50926.61  48535.78  47120.89  43742.75 
##       474       475       476       477       478       479       480       481 
##  47501.65  54970.48  49393.33  51097.82  45882.82  44260.63  47095.66  36502.08 
##       482       483       484       485       486       487       488       489 
##  30058.89  31929.59  34628.25  36119.32  37085.89  30722.00  43261.47  49926.57 
##       490       491       492       493       494       495       496       497 
##  56761.88  51469.46  56306.07  63945.36  67759.57  54005.78  44576.70  42600.23 
##       498       499       500       501       502       503       504       505 
##  42923.67  43715.41  38198.08  40598.77  45904.18  51589.97  52300.16  52410.81 
##       506       507       508       509       510       511       512       513 
##  46108.11  47449.07  43705.54  46463.02  46128.46  39839.63  40971.54  40146.08 
##       514       515       516       517       518       519       520       521 
##  41252.52  43894.48  36729.36  32005.27  55912.45  64079.11  67734.72  61108.96 
##       522       523       524       525       526       527       528       529 
##  62449.42  76045.00  83026.79  57958.73  52825.04  49516.96  53899.13  53405.10 
##       530       531       532       533       534       535       536       537 
##  43581.53  48576.99  61246.70  55763.59  59163.70  63154.37  60204.83  55231.98 
##       538       539       540       541       542       543       544       545 
##  48689.51  47344.42  55287.30  55037.54  47592.18  49817.42  49643.96  50338.30 
##       546       547       548       549       550       551       552       553 
##  40980.53  32810.37  37155.33  45277.78  45074.60  46781.71  40787.22  49806.68 
##       554       555       556       557       558       559       560       561 
##  50962.82  40734.64  50282.48  58150.92  57515.41  61115.70  56879.89  68648.85 
##       562       563       564       565       566       567       568       569 
##  85349.44  75433.33  63988.69  68411.07  66534.13  67721.61  59285.58  43263.21 
##       570       571       572       573       574       575       576       577 
##  50276.86  56186.18  57369.80  59446.80  60094.31  57218.69  69474.05  58839.89 
##       578       579       580       581       582       583       584       585 
##  52558.42  60165.36  61670.42  54759.55  61030.53  56603.53  53646.00  67225.71 
##       586       587       588       589       590       591       592       593 
##  52643.96  59993.40  59085.15  52802.93  52107.60  52383.83  43093.13  45891.94 
##       594       595       596       597       598       599       600       601 
##  40514.60  44763.40  53532.86  46859.39  52722.51  55101.98  60775.03  56930.47 
##       602       603       604       605       606       607       608       609 
##  61747.60  53310.32  55191.64  55968.52  58278.30  58852.75  58393.19  52566.53 
##       610       611       612       613       614       615       616       617 
##  59633.13  57691.86  54787.03  51490.82  44448.83  55967.14  59857.29  50786.37 
##       618       619       620       621       622       623       624       625 
##  61195.63  65383.48  58868.73  81111.76  66224.17  58548.17  60552.49  55902.58 
##       626       627       628       629       630       631       632       633 
##  46231.24  56946.08  37488.88  37349.02  46923.95  57413.88  55457.13  84206.04 
##       634       635       636       637       638       639       640       641 
##  74385.46  76587.73  78226.15  72948.84  65663.09  62295.07  50191.69  48558.88 
##       642       643       644       645       646       647       648       649 
##  47449.70  45930.90  44314.46  46993.13  51613.70  66594.82  81028.96  78150.35 
##       650       651       652       653       654       655       656       657 
##  79054.73  84930.22  98384.05  93139.09  63388.90  60838.97  57791.27  58776.57 
##       658       659       660       661       662       663       664       665 
##  55215.12  45621.33  48007.29  52328.01  51519.66  63089.31  62966.93  63256.65 
##       666       667       668       669       670       671       672       673 
##  51704.24  53064.68  54083.83  49419.43  43395.09  46432.05  44029.23  47518.38 
##       674       675       676       677       678       679       680       681 
##  45269.29  38102.04  32756.43  32753.93  35504.79  40239.99  42501.95  40505.11 
##       682       683       684       685       686       687       688       689 
##  40528.71  40977.78  35316.34  41650.01  41147.12  41446.58  43443.42  54158.38 
##       690       691       692       693       694       695       696       697 
##  62621.38  70676.03  59965.68  55970.77  52851.01  58008.56  48294.88  41989.69 
##       698       699       700       701       702       703       704       705 
##  35881.30  32598.20  31077.16  36587.50  34849.92  35523.02  41459.95  70138.67 
##       706       707       708       709       710       711       712       713 
##  76301.38  93860.27  90177.06  92774.06 107809.25 106649.12  83995.86  84343.41 
##       714       715       716       717       718       719       720       721 
##  75675.98  72776.76  70751.95  53470.17  48865.46  52359.86  49863.00  38970.96 
##       722       723       724       725       726       727       728       729 
##  44542.61  40811.44  43058.04  40931.83  31633.00  35586.33  36211.98  29843.35 
##       730       731       732       733       734       735       736       737 
##  47922.87  50171.96  48204.60  53862.17  46263.83  46538.57  54525.15  40967.92 
##       738       739       740       741       742       743       744       745 
##  37377.61  45884.32  42656.80  44160.35  46930.82  46043.42  42460.24  49453.52 
##       746       747       748       749       750       751       752       753 
##  44471.43  65447.04  70770.68  66877.55  58807.79  78599.29  71669.20  70587.73 
##       754       755       756       757       758       759       760       761 
##  55815.67 104566.37 121647.60 126239.44 107753.81 110183.60 109430.57 107458.59 
##       762       763       764       765       766       767       768       769 
##  60106.92  45148.65  47059.07  45682.77  43658.09 152296.92 156736.54 181956.45 
##       770       771       772       773       774       775       776       777 
## 185525.15 179459.10 177455.27 184341.66  81496.14  72079.55  38433.60  41868.68 
##       778       779       780       781       782       783       784       785 
##  42277.91  46678.31  41073.69  41393.88  41236.41  45792.79  40526.84  40224.13 
##       786       787       788       789       790       791       792       793 
##  45341.22  48367.81  46621.24  43968.91  46709.28  50078.92  50485.03  45025.40 
##       794       795       796       797       798       799       800       801 
##  41058.96  41644.27  42054.62  36683.91  36765.70  36037.90  35928.51  47537.74 
##       802       803       804       805       806       807       808       809 
##  50268.61  56885.14  59034.95  53595.92  60762.42  68500.99  57364.68  50434.58 
##       810       811       812       813       814       815       816       817 
##  44283.11  48095.45  52466.26  50635.89  38637.90  36941.41  44523.88  52879.36 
##       818       819       820       821       822       823       824       825 
##  44525.38  38905.77  32608.07  43792.08  43970.66  41374.15  35542.63  44713.95 
##       826       827 
##  52763.47  57229.43 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8104
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    5.045113   0.7769986    3.921777
## t2* 2662.578983 166.2596891  880.853249
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.097096       5.000461   13.28527
## 2    lag_depvar 1610.509920    2713.203396 4493.78602

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Mar 24 00:52:36 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Mar 24 00:52:45 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Mar 24 00:52:54 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Mar 24 00:53:04 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Mar 24 00:53:13 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Mar 24 00:53:22 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Mar 24 00:53:32 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Mar 24 00:53:41 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Mar 24 00:53:50 2025
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Mar 24 00:54:00 2025
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_25<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2025",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2020",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_25 %>% 
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
Item 2025 2024 2023 2022 2021 2020
Agua 0.0000 6.993667 5.195333 5.410333 5.849167 9.93775
Comida 258.8850 326.890000 366.009167 312.386750 317.896583 392.93367
Comunicaciones 0.0000 0.000000 0.000000 0.000000 0.000000 0.00000
Electricidad 59.9440 83.582750 38.104750 47.072333 29.523000 20.60458
Enceres 0.0000 23.989000 18.259750 24.219750 14.801167 39.01200
Farmacia 0.0000 0.000000 10.704083 2.835000 13.996083 14.03675
Gas/Bencina 0.0000 44.292667 42.636000 45.575000 13.583667 17.25833
Diosi 36.9995 33.319583 55.804250 31.180667 52.687833 37.12133
donaciones/regalos 0.0000 0.000000 0.000000 0.000000 0.000000 0.00000
Electrodomésticos/ Mantención casa 0.0000 0.000000 0.000000 0.000000 0.000000 0.00000
VTR 10.9950 18.326667 12.829167 25.156667 19.086917 19.11375
Netflix 0.0000 1.391417 8.713833 7.151583 7.028750 8.24725
Otros 0.0000 76.164000 5.481667 5.000000 0.000000 0.00000
Total 366.8235 614.949750 563.738000 505.988083 474.453167 558.26542
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")

tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2655, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)
    

fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
    dplyr::rename(ds = date3, y = value) %>%
    dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
    dplyr::mutate(unique_id = "serie_1")%>%
    dplyr::select(unique_id, ds, y)

# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
  uf_timegpt,
  h = 298,               # 298 días a pronosticar
  freq = "D",            # Frecuencia diaria
  add_history = TRUE,     # Incluir datos históricos en el output
  level = c(80,95),
  model=  "timegpt-1-long-horizon", 
  clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>% 
    mutate(ds = as.Date(ds))

timegpt_fcst <- timegpt_fcst %>% 
    mutate(ds = as.Date(ds))

# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
    uf_timegpt %>% mutate(type = "Histórico"),
    timegpt_fcst %>% mutate(type = "Pronóstico")
)

# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
    # Intervalo de confianza del 95%
    geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`), 
                fill = "#4B9CD3", alpha = 0.2) +
    # Intervalo de confianza del 80%
    geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`), 
                fill = "#4B9CD3", alpha = 0.3) +
    # Línea histórica
    geom_line(data = filter(full_data, type == "Histórico"), 
              aes(color = "Histórico"), size = 1) +
    # Línea de pronóstico
    geom_line(data = filter(full_data, type == "Pronóstico"), 
              aes(color = "Pronóstico"), size = 1) +
    # Línea vertical separadora
    geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds), 
               linetype = "dashed", color = "red", size = 0.8) +
    # Configuración del eje x
    scale_x_date(
        date_breaks = "3 months",  # Reduce la frecuencia de las etiquetas
        date_labels = "%b %Y",  # Formato de etiquetas (mes y año)
    ) +
    # Configuración del eje y
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
    # Configuración de colores
    scale_color_manual(
        name = "Leyenda",
        values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
    ) +
    # Títulos y subtítulos
    labs(
        title = "Pronóstico de Serie Temporal con TimeGPT",
        subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
        x = "Fecha",
        y = "Valor",
        color = "Leyenda"
    ) +
    # Tema y estilos
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
    )
## Warning: Removed 2655 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
## 
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
## 
##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
## 
##     invoke
## The following object is masked from 'package:data.table':
## 
##     :=
  model <- prophet(
  cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
  # Trend flexibility
  growth = "linear",
  changepoint.prior.scale = 0.05,  # Reduced for smoother trend
  n.changepoints = 50,  # Increased from default 25
  
  # Seasonality
  yearly.seasonality = TRUE,
  weekly.seasonality = TRUE,
  daily.seasonality = FALSE,  # Disabled for daily data
  seasonality.mode = "additive",
  seasonality.prior.scale = 15,  # Increased to capture stronger seasonality
  
  # Holidays (if applicable)
  # holidays = generated_holidays  # Create with add_country_holidays()
  
  # Uncertainty intervals
  interval.width = 0.95,
  uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)

ggplot(forecast, aes(x = ds, y = yhat)) +
  geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper), 
              fill = "#9ecae1", alpha = 0.4) +
  geom_line(color = "#08519c", linewidth = 0.8) +
  geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
  scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
  scale_y_continuous(labels = scales::comma) +
  labs(title = "Valores predichos (95%IC)",
      # subtitle = "March 10, 2025 - May 7, 2025",
       x = "Fecha",
       y = "Valor",
      # caption = "Source: Prophet Forecast Model"
      ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    plot.caption = element_text(color = "gray30")
  )

La proyección de la UF a 298 días más 2025-04-09 00:04:58 sería de: 26.676 pesos// Percentil 95% más alto proyectado: 35.051,67

Según TimeGPT: La proyección de la UF a 298 días más 2026-02-01 sería de: 40.421,82 pesos// Percentil 80% más alto proyectado: 40.735,26 pesos// Percentil 95% más alto proyectado: 41.080,7

Según prophet: La proyección de la UF a 298 días más 2026-02-01 sería de: 39.289 pesos// Percentil 95% más alto proyectado: 42.622

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 26293.96 26328.60
Lo.80 26425.51 26491.43
Point.Forecast 26675.83 26799.01
Hi.80 31453.43 32108.70
Hi.95 34319.77 34919.48


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,1,1) 
## 
## Coefficients:
##          ar1      ma1
##       0.4188  -0.9485
## s.e.  0.1294   0.0592
## 
## sigma^2 = 37557:  log likelihood = -481.1
## AIC=968.2   AICc=968.55   BIC=975.03
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.4055   562.8354  14.9430
## s.e.  0.1106   328.3026  10.1052
## 
## sigma^2 = 35910:  log likelihood = -484.98
## AIC=977.96   AICc=978.55   BIC=987.12
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 669.6808 643.7799 629.0613
Lo.80 810.3198 795.5792 723.3464
Point.Forecast 1075.9932 1083.7927 948.1711
Hi.80 1341.6666 1375.4911 1241.8993
Hi.95 1482.3056 1529.9070 1432.1579


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)

Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))

Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")

Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))

Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
  mutate(value = Tami + Andrés) %>%
  rename(ds = mes_ano, y = value) %>%
  mutate(#ds= format(ds, "%Y-%m"),
         unique_id = "1") %>% #it is only one series
  select(unique_id, ds, y)

#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
  dplyr::rename(ds = date3, y = value) %>%
  dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
  dplyr::mutate(unique_id = "serie_1")%>%
  dplyr::select(unique_id, ds, y) %>%
  mutate(ds = ymd(ds)) %>%  # Convert 'ds' to Date
  mutate(month = month(ds), year = year(ds)) %>%  # Extract month and year
  group_by(month, year) %>%  # Group by month and year
  summarise(average_y = mean(y))%>%  # Calculate average y
  mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
  ungroup()%>%
  select(ds, uf=average_y)

Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |> 
  dplyr::left_join(uf_timegpt_my, by=c("ds"="ds")) 

#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
  Gastos_casa_mensual_2022_timegpt_ex,
  h = 12,
  freq = "M",  # Monthly frequency
  add_history = TRUE,
  level = c(80, 95),
  model = "timegpt-1",#"timegpt-1-long-horizon",
  clean_ex_first = TRUE
)

# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

# Combine historical and forecast data
full_data_gastos <- bind_rows(
  Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
  gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)

full_data_gastos |> 
  dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |> 
# Visualize results
ggplot(aes(x = ds, y = y)) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
              fill = "#4B9CD3", alpha = 0.2) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
              fill = "#4B9CD3", alpha = 0.3) +
  geom_line(aes(color = type), linewidth = 1.5) +
  geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
             linetype = "dashed", color = "red", linewidth = 0.8) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b %Y"
  ) +
  scale_y_continuous(
    name = "Gastos Totales",
    labels = scales::comma,
    breaks = pretty(full_data_gastos$y, n = 10),
    expand = expansion(mult = c(0.05, 0.05))
  ) +
  scale_color_manual(
    name = "Leyenda",
    values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
  ) +
  labs(
    title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
    subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
    x = "Fecha",
    y = "Gastos Totales",
    color = "Leyenda"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.title.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    legend.position = "bottom",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## system code page: 65001
## 
## time zone: UTC
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] prophet_1.0         rlang_1.1.5         Rcpp_1.0.14        
##  [4] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [7] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.12     
## [10] tidytext_0.4.2      DT_0.33             janitor_2.2.1      
## [13] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [16] xts_0.14.1          forecast_8.23.0     wordcloud_2.6      
## [19] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-16          
## [22] NLP_0.3-2           tsibble_1.1.6       lubridate_1.9.4    
## [25] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.4        
## [28] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [31] gsynth_1.2.1        sjPlot_2.8.17       lattice_0.22-6     
## [34] GGally_2.2.1        ggplot2_3.5.1       gridExtra_2.3      
## [37] plotrix_3.8-4       sparklyr_1.9.0      httr_1.4.7         
## [40] readxl_1.4.5        zoo_1.8-13          stringr_1.5.1      
## [43] stringi_1.8.4       DataExplorer_0.8.3  data.table_1.17.0  
## [46] reshape2_1.4.4      fUnitRoots_4040.81  plyr_1.8.9         
## [49] readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] bitops_1.0-9        cellranger_1.1.0    datawizard_1.0.1   
##   [4] httr2_1.1.1         lifecycle_1.0.4     StanHeaders_2.32.10
##   [7] doParallel_1.0.17   globals_0.16.3      vroom_1.6.5        
##  [10] MASS_7.3-60.2       insight_1.1.0       crosstalk_1.2.1    
##  [13] magrittr_2.0.3      sass_0.4.9          rmarkdown_2.29     
##  [16] jquerylib_0.1.4     yaml_2.3.10         fracdiff_1.5-3     
##  [19] doRNG_1.8.6.1       askpass_1.2.1       pkgbuild_1.4.6     
##  [22] DBI_1.2.3           abind_1.4-8         quadprog_1.5-8     
##  [25] nnet_7.3-19         rappdirs_0.3.3      sandwich_3.1-1     
##  [28] inline_0.3.21       tokenizers_0.3.0    listenv_0.9.1      
##  [31] anytime_0.3.11      performance_0.13.0  spatial_7.3-17     
##  [34] parallelly_1.42.0   codetools_0.2-20    xml2_1.3.8         
##  [37] tidyselect_1.2.1    ggeffects_2.2.1     farver_2.1.2       
##  [40] urca_1.3-4          its.analysis_1.6.0  matrixStats_1.5.0  
##  [43] stats4_4.4.0        jsonlite_1.9.1      ellipsis_0.3.2     
##  [46] Formula_1.2-5       iterators_1.0.14    systemfonts_1.2.1  
##  [49] foreach_1.5.2       tools_4.4.0         glue_1.8.0         
##  [52] xfun_0.51           TTR_0.24.4          ggfortify_0.4.17   
##  [55] loo_2.8.0           withr_3.0.2         timeSeries_4041.111
##  [58] fastmap_1.2.0       boot_1.3-30         openssl_2.3.2      
##  [61] caTools_1.18.3      digest_0.6.37       timechange_0.3.0   
##  [64] R6_2.6.1            lfe_3.1.1           colorspace_2.1-1   
##  [67] networkD3_0.4       gtools_3.9.5        generics_0.1.3     
##  [70] htmlwidgets_1.6.4   ggstats_0.9.0       pkgconfig_2.0.3    
##  [73] gtable_0.3.6        timeDate_4041.110   lmtest_0.9-40      
##  [76] selectr_0.4-2       janeaustenr_1.0.0   htmltools_0.5.8.1  
##  [79] carData_3.0-5       tseries_0.10-58     snakecase_0.11.1   
##  [82] knitr_1.50          rstudioapi_0.17.1   tzdb_0.5.0         
##  [85] uuid_1.2-1          nlme_3.1-164        curl_6.2.1         
##  [88] cachem_1.1.0        sjlabelled_1.2.0    KernSmooth_2.23-22 
##  [91] parallel_4.4.0      fBasics_4041.97     pillar_1.10.1      
##  [94] vctrs_0.6.5         gplots_3.2.0        slam_0.1-55        
##  [97] car_3.1-3           dbplyr_2.5.0        xtable_1.8-4       
## [100] evaluate_1.0.3      mvtnorm_1.3-3       cli_3.6.4          
## [103] compiler_4.4.0      crayon_1.5.3        rngtools_1.5.2     
## [106] future.apply_1.11.3 labeling_0.4.3      sjmisc_2.8.10      
## [109] rstan_2.32.7        QuickJSR_1.6.0      viridisLite_0.4.2  
## [112] assertthat_0.2.1    munsell_0.5.1       lazyeval_0.2.2     
## [115] Matrix_1.7-0        sjstats_0.19.0      hms_1.1.3          
## [118] bit64_4.6.0-1       future_1.34.0       nixtlar_0.6.2      
## [121] extraDistr_1.10.0   igraph_2.1.4        RcppParallel_5.1.10
## [124] bslib_0.9.0         quantmod_0.4.26     bit_4.6.0
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))